Satellite-based positioning system improvement

ABSTRACT

A method, device and system for determining a receiver location using weak signal satellite transmissions. The invention involves a sequence of exchanges between an aiding source and a receiver that serve to provide aiding information to the receiver so that the receiver&#39;s location may be determined in the presence of weak satellite transmissions. With the aiding information, the novel receiver detects, acquires and tracks weak satellite signals and computes position solutions from calculated pseudo ranges despite the inability to extract time synchronization date f, &#39;n the weak satellite signals. The invention includes as features, methods and apparatus for the calibration of a local oscillator, the cancellation of cross correlations, a Doppler location scheme, an ensemble averaging scheme, the calculation of almanac aiding from a table of orbit coefficients, absolute time determination, and a modified search engine.

This application is a divisional of application Ser. No. 10/530,170filed Sep. 19, 2005 now U.S. Pat. No. 7,463,189, which was a NationalStage Entry of PCT application number PCT/US03/31684 filed Oct. 04,2003, which claims the priority filing date of U.S. ProvisionalApplication Ser. No. 60/416,367 filed on Oct. 4, 2002.

FIELD OF THE INVENTION

This invention relates to the design of receivers employed insatellite-based positioning systems (SPS) such as the US Navstar GlobalPositioning System (GPS), the Russian Global Navigation Satellite System(GLONASS) and the European Galileo system. More specifically, theinvention relates to methods, devices and systems for determining areceiver location using weak signal satellite transmissions.

BACKGROUND OF THE INVENTION

Satellite based positioning systems operate by utilizing constellationsof satellites which transmit to earth continuous direct sequence spreadspectrum signals. Receivers within receiving range of these satellitesintercept these signals which carry data (navigation messages) modulatedonto a spread spectrum carrier. This data provides the precise time oftransmission at certain instants in the signal along with orbitalparameters (e.g., precise ephemeris data and less precise almanac datain the case of GPS) for the satellites themselves. By estimating thetime of flight of the signal from each of four satellites to thereceiver and computing the position of the satellites at the times oftransmission corresponding to the estimated times of flight it ispossible to determine the precise location of the receiver's antenna.

In a conventional SPS receiver, the process by which this is doneinvolves estimating pseudoranges of at least 4 satellites and thencomputing from these the precise location and clock error of thereceiver. Each pseudorange is computed as the time of flight from onesatellite to the receiver multiplied by the speed of light and is thusan estimate of the distance or ‘range’ between the satellite and thereceiver. The time of flight is estimated as the difference between thetime of transmission determined from the navigation message and the timeof receipt as determined using a clock in the receiver. Since thereceiver's clock will inevitably have a different present time whencompared to the clock of the satellites, the four range computationswill have a common error. The common error is the error in thereceiver's clock multiplied by the speed of light.

By using at least 4 satellites it is possible to solve a set ofequations to determine both the receiver clock error and the location ofthe antenna. If only 3 measurements are available it is still possibleto determine the location and clock error provided at least one of thereceiver's coordinates is already known. Often, this situation can beapproximated by estimating the altitude of the antenna.

The signals from the satellites consist of a carrier signal which isbiphase modulated by a pseudo-random binary spreading code at arelatively high “chipping” rate (e.g., 1.023 MHz) and then biphasemodulated by the binary navigation message at a low data rate (e.g., 50Hz). The carrier to noise ratio is typically very low (e.g., 31 dBHz to51 dBHz) at the earth's surface for a receiver with unobstructed line ofsight to the satellite from its antenna. However, it is sufficient topermit the signals to be detected, acquired and tracked usingconventional phase-locked loop and delay-locked loop techniques and forthe data to be extracted.

The process of tracking the code of a signal in a conventional SPSreceiver involves the use of a hardware code generator and signal mixer.When the locally generated code is exactly aligned with that of theincoming signal, the output from the mixer contains no code modulationat all. Hence the bandwidth of the signal is much less and it can befiltered to greatly increase the signal to noise ratio. This is usuallydone using a decimation filter such that the correlator output samplingrate is much lower than the input sampling rate (e.g., 1 kHz at theoutput compared to 1.3 MHz at the input).

Also, in the case of GPS, the precise time of transmission of thissignal corresponding to any given instant at the receiver can bedetermined by latching the state of the code generator to get the codephase and by counting the code epochs within each bit of the data and bycounting the bits within each word of the navigation message and bycounting the words within each subframe of the message and by extractingand decoding the times of transmission corresponding to the subframeboundaries. A similar scheme can be used for any SPS.

However, traditional SPS receivers can suffer from troublesome lapses inposition identification in the presence of weakened transmissionsignals. When the direct line of sight between the antenna and thesatellites is obstructed, signals may be severely attenuated when theyreach the antenna. Conventional techniques can not be used to detect,acquire and track these signals. Moreover, under these circumstanceseven if the signal could be detected, the carrier-to-noise ratio of aGPS signal, for example, may be as low as or lower than 24 dBHz and assuch it is not possible to extract the data from the signals.

Prior art devices have attempted to minimize or overcome theseshortcomings through the use of aiding, information. In such schemes,additional information is externally supplied to the SPS receiversthrough various secondary transmission sources to balance the shortfallof information resulting from the attenuated signals. Examples of suchdevices are taught in the patents to Taylor et al. (U.S. Pat. No.4,445,118) (aided by satellite almanac data); Lau (U.S. Pat. No.5,418,538) (aided by differential satellite positioning information andephemerides); Krasner (U.S. Pat. No. 5,663,734) (aided by transmissionof Doppler frequency shifts); Krasner (U.S. Pat. No. 5,781,156) (aidedby transmission of Doppler frequency shifts); Krasner (U.S. Pat. No.5,874,914) (aided by Doppler, initialization and pseudorange data)Krasner (U.S. Pat. No. 5,841,396) (aided by satellite almanac data);Loomis, et al. (U.S. Pat. No. 5,917,444) (aided by selected satelliteephemerides, almanac, ionosphere, time, pseudorange corrections,satellite index and/or code phase attributes); Krasner (U.S. Pat. No.5,945,944) (aided by timing data); Krasner (U.S. Pat. No. 6,016,119)(aided by retransmission of data from satellite signal)

However, aiding information requires additional transmissioncapabilities. For example, aiding information may be sent to the SPSreceiver using additional satellite transmitters or wireless telephonesystems. As such, it is a significant advantage to reduce the quantum ofaiding information supplied to limit the use of such additionalresources. For example, when the voice path of a wireless communicationnetwork is being used to communicate the aiding information, the voicecommunication will be interrupted by the aiding message. The aidingmessages must therefore be as short as possible in order to limit thevoice interruptions to tolerable durations and frequencies. Also, nomatter how the aiding data is communicated, its communication will delaythe operation of the receiver. In many applications the location data isneeded promptly and therefore any delay must be minimized.

BRIEF DESCRIPTION OF THE INVENTION

The present invention is an improvement on the invention disclosed inInternational Patent Application PCT/AU01/00519.

Calibration of Local Oscillator

In many assisted GPS applications rapid acquisition of satellite signalsis a key requirement. Acquisition is delayed because of drift of thereference oscillator in the receiving unit. The relative velocity alongthe line of sight from the receiver to the satellite induces a Dopplershift in the frequency of received signal. The Doppler shift containsuseful information on the velocity of the receiver antenna, but thepresence of the Doppler shift necessitates a frequency search thatincreases the time for acquisition. Reference oscillator drift is amajor contributor to lengthening acquisition time as it causes the“Doppler” frequency search to be increased to allow for referenceoscillator drift. By utilizing the precise signal framing of a digitalcommunications link, the invention calibrates a local oscillator andthus reduce the effect of drift. This is accomplished by counting localoscillator cycles and fractions thereof over a period preciselydetermined by a number of signal framing intervals. Once the calibrationoffset is determined it is used as a correction by the GPS receiverfirmware when performing acquisition searches or it can be used tocorrect the oscillator frequency so as to minimize the offset.

Cancellation of Cross Correlation

Another aspect of the invention is the reduction of cross correlationsbetween weak and strong signals experienced at correlator outputs. Thesecross-correlations are inherent limitations of the GPS C/A Codestructure. The cross correlations between codes at certain code andDoppler offsets are only 20 dB between the peak of the autocorrelationmain lobe. At the correlator output these cross-correlations areindistinguishable from correlations with the locally generated replicaof the weak signal being sought. Reducing the level ofcross-correlations caused by a strong signal will reduce its jammingeffect on weaker signals. In this way the usable dynamic range isincreased to permit weak signals to be acquired, tracked and used in thepresence of strong ones. At least 3 satellites are needed to make a 2Dfix and 4 satellites are require to make a 3D fix. Furthermore, morethan the minimum number may be required to obtain a low enough DilutionOf Precision to permit an accurate fix to be made. Hence the ability touse more of the signals present is an advantage. In urban canyons thisadvantage will be distinct in that there are often only one or twostrong signals present and these jam all of the weaker ones. The biggestproblem with the concept of canceling the strong signals is that thesignals are represented with very low precision at the input to acorrelator and, hence, any scaling of the signal can only be extremelycrudely performed. This threatens the viability of the concept. Thepresent invention provides allows the scaling to be performed at a pointwhere the signal is represented by 10 bit samples and scaling is muchmore feasible.

Doppler Location Scheme

Another aspect of the invention deals with the problem that in a weaksignal environment, it is not possible to resolve code phase ambiguitywithout knowing the GPS receiver's initial position to within 150 km.The 150 km is the distance a satellite signal would travel in the timeoccupied by one half of a code epoch. The invention provides a methodfor computing the initial position autonomously without requiring priorknowledge of the location by computing it from measured satelliteDoppler differences. Using differences to perform the calculationensures that any dependence on the current local oscillator offset isremoved from the calculation.

Ensemble Averaging Scheme

A still further aspect of the invention concerns the limitations onsensitivity caused by limitation in performing Fourier transforms. Thetransform involves accumulating values in frequency bins. Previousalgorithms are limited in sensitivity because the reduction of binwidthis offset by squaring losses. This causes growth of the FFT (fastFourier transform) size to impractical lengths. To overcome this problema spectral averaging algorithm is employed for integration periodsbeyond 160 ms (FFT length of 128). To avoid excessive memory usage forstoring FFT arrays, the invention operates in the fashion of a movingaverage by filtering the squared magnitudes of the FFT bins in the samefashion as for the autoconvolutions when acquiring.

Calculation of Almanac Aiding from Table of Orbit Coefficients

A still further aspect of the invention reduces the traffic from thereceiver to the aiding source. In particular, an alternative is providedto having the receiver acquire its own almanac data and requestingaugmentation coefficients for a specific Issue Of Almanac as aidinginformation. Instead the invention hardcodes orbital coefficients into alookup table and the aiding information is provided as broadcasts atregular intervals.

Absolute Time Determination

Another aspect of the invention concerns the problem of extracting datafrom the GPS signal when there is a poor signal to noise ratio. Oftenthe output of a weak signal GPS receiver is limited to code phases foreach satellite rather than a full time-of-transmit (pseudorange).However a full time-of-transmit is obtainable from a partial code phaseprovided that a reference time stamp for the observation is available,as well as an estimate of the users location. The estimate of the userslocation can be used to calculate a nominal time of flight for eachsatellite provided that its uncertainty is less than 150 km (0.5 ms),while the reference time stamp is used to convert the time-of-receiptinto an estimated satellite time-of-transmit. The code phase is thenused to refine the time-of-transmit estimate into an ambiguity resolvedtime-of-transmit. This ambiguity resolved time-of-transmit will beconsistent with the true user position and hence can be used tocalculate the true user position.

Modified Search Engine

A still further aspect of the invention is a modified search engine.Modern GPS receivers often incorporate massively parallel correlationhardware to speed the acquisition process by concurrently searchingacross a broad range of codephases and/or Doppler frequencies. Suchhardware are sometimes referred to as search engines. The signalprocessing algorithms utilized in the invention can be incorporated intoa modified search engine to achieve even faster searching by reducingthe length of integration period required.

An object of the present invention is to provide a satellite-basedpositioning system receiver for weak signal operation have each of theadvantages just described.

Consistent with these objectives, a device made in accordance with thisinvention utilizes a novel signal processing scheme for detecting,acquiring and tracking attenuated satellite signals, such as those thatmight be received at an indoor location, and computes locationsolutions. The scheme makes novel use of attenuated satellite signalsand minimal externally-supplied aiding information.

BRIEF DESCRIPTION OF THE DRAWINGS

This invention is illustrated by means of the accompanying drawings.However, these figures represent examples of the invention and do notserve to limit its applicability.

FIG. 1 is a sequence diagram describing the interactions between anaiding source, a call taker and a handset with an integrated SPSreceiver according to one embodiment of this invention;

FIG. 2 is a flowchart describing the overall algorithm according to oneembodiment of this invention for acquiring satellite signals, measuringcode phases, carrier smoothing these measurements, computing pseudorangedifferences and computing handset location;

FIG. 3 is a block diagram of a typical SPS receiver according to thisinvention;

FIG. 4 is a block diagram describing the signal processing algorithmused to measure amplitude in each of an early and a late arm of eachchannel of the correlator according to one embodiment of this invention;

FIG. 5 is a block diagram describing the carrier smoothing algorithmused to reduce the error in the code phase measurements according to oneembodiment of this invention;

FIG. 6 is a block diagram describing the algorithm used to compute thelocation and velocity from the code phase and carrier frequencydifferences according to one embodiment of this invention.

FIG. 7 is a schematic diagram of a canceler integrated into acorrelator.

FIG. 8 illustrates a 2 bin search engine architecture.

FIG. 9 is a schematic representation of a 2 codephase modified searchengine.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

This invention relates to refinements and extensions to a commonly ownedinvention disclosed in U.S. Pat. No. 5,459,473. Accordingly, theforegoing U.S. patent is hereby incorporated by reference.

Aiding Source/Receiver Interaction

As previously described, the aiding data used in accordance with thepresent invention may be limited to information that includes anapproximate location for an SPS receiver and the positions andvelocities of a specific set of satellites. This information isdetermined and provided through a request/response sequence. A model ofone embodiment of such an exchange in accordance with the presentinvention is depicted in FIG. 1.

A typical exchange might involve an SPS Receiver 1, an Aiding Source 2and a Call Taker 3. For instance, the SPS Receiver 1 might be a GPSreceiver embedded in or co-located with a wireless telephone or otherhandset. The Aiding Source 2 may be located at a call center or cellsite or elsewhere in the wireless network such that the aiding data istransmitted via a wireless communication link to the handset. The CallTaker 3 may also be located at the call center or other locationaccessible from the wireless network. The ultimate user of the locationdata may be either the Call Taker 3 or the user accompanying the SPSReceiver 1. Other forms of transmission between the SPS Receiver 1,Aiding Source 2, and the Call Taker 3 may be utilized without departingfrom the objectives of the present invention.

To begin the exchange, the SPS Receiver 1 sends a First Aiding Request 4to the Aiding Source 2. This would typically occur upon activation ofthe SPS Receiver 1 but may occur at other times as well. In response,the Aiding Source 2, sends a First Aiding Response 5 which contains theapproximate location of the SPS Receiver 1. Preferably, the approximatelocation of the SPS Receiver 1 is accurate to better than one half of acode epoch of a satellite signal multiplied by the speed of light orabout 150 km in the case of GPS. The approximate location may also besent to the Call Taker 3 in a First Aiding Report 6.

Doppler Location Scheme Alternative

In a weak signal environment, it is not possible to resolve code phaseambiguity without knowing the GPS receiver's initial position to within150 km. A method for computing the initial position autonomously withoutrequiring prior knowledge of the location is to compute it from measuredsatellite Doppler differences. Using differences to perform thecalculation ensures that any dependence on the current local oscillatoroffset is removed from the calculation. A Doppler positioning algorithmthat implements this scheme is the following, which would be executed bymicroprocessor 20 of FIG. 3:

-   1. For each input observation made by the GPS receiver apply the    space vehicle (SV) clock corrections to both the satellite time of    transmits (‘Toti’) and the satellite Doppler frequencies.-   2. For each satellite ‘i’ calculate satellite position Xi (Xi,Yi,Zi)    at the corrected satellite time of transmit ‘Toti’.-   3. Calculate the average satellite position by taking the vector    mean of the calculated satellite positions. Convert the vector mean    to a unit vector and then scale by the radius of the earth. This    provides an initial estimate for the user position Xu (Xu,Yu,Zu).    The user's local velocity is assumed to be zero throughout the    entire process, so (XuDot,YuDot,ZuDot) is (0,0,0).-   4. Iterate through the following steps 5 to 10 until the residual    error is sufficiently small (say less than 100 m).-   5. Using the current estimate of the user position and user velocity    (zero), calculate the range ‘Ri’ and range-rate ‘RRi’ vectors to the    satellite ‘i’. Note that these will change at each iteration as the    estimate of the user position Xu converges to the true position.-   6. Using the range vector ‘Ri’ calculate the line-of-sight vector    ‘LOSi’ (the unit range-vector). Calculate the projection of the    range-rate vector onto the line of sight vector (RRi.LOSi) thereby    determining an estimate of the predicted satellite range-rate.    Finally calculate a range-rate prediction vector ‘yh’ as the vector    of differences between the first satellite range rate and all    remaining range-rates.-   7. Using the measured satellite observations, calculate a measured    range-rate vector ‘y’ as the differences between the first measured    satellite range-rate (Doppler frequency scaled to same units as    predictions above).-   8. Using the satellite line-of-sight vectors calculate a measurement    matrix ‘M’ of line of sight vector differences between the first    satellite and all remaining satellites. Each row in the matrix    corresponds to the difference between the first satellite LOS vector    and the one of the remaining satellite LOS vectors.-   9. Calculate the pseudo-inverse of the M matrix ‘Mpi’.-   10. Correct the current estimate of the user position by calculating    dx=Mpi*(y−yh) and Xu=Xy+dx. When the magnitude of the correction    ‘dx’ is less than a sufficiently small threshold (say less than    100 m) then terminate the iteration process.    Absolute Time Determination

Related to the Doppler position process is the following aspect of theinvention. One of the limitations of assisted GPS in a weak signalenvironment is the inability to extract data from the GPS signal due tothe poor signal to noise ratio. This means that the output of a weaksignal GPS receiver is often limited to code phases for each satelliterather than a full time-of-transmit (pseudorange). However a fulltime-of-transmit can be obtained from a partial code phase provided thata reference time stamp for the observation is available, as well as anestimate of the users location.

The estimate of the users location can be used to calculate a nominaltime of flight for each satellite provided that its uncertainty is lessthan 150 km (0.5 ms), while the reference time stamp is used to convertthe time-of-receipt into an estimated satellite time-of-transmit. Thecode phase is then used to refine the time-of-transmit estimate into anambiguity resolved time-of-transmit. This ambiguity resolvedtime-of-transmit will be consistent with the true user position andhence can be used to calculate the true user position.

For the above process to provide acceptable positional accuracy it isnecessary that the reference time-stamp have an error of less than 10ms. The algorithm that is provided below provides a means by which anyerror in the estimated time-of-receipt can be calculated providing thatat least one additional satellite is available for use. This algorithmis similar in concept to the Doppler position process except that fullor ambiguity resolved time-of-transmits are employed.

The algorithm requires an additional redundant satellite becauseattempting to estimate the time-of-receipt error is equivalent tointroducing an additional degree of freedom or an additional unknown andhence an additional linearly independent observation is required forthis to be estimated. If only 4 observations are available then therewill only be a single position solution to the problem regardless of thetime-of-receipt and the time-of-receipt error will always be calculatedas zero.

The algorithm is the following:

-   1. For each raw input observation from satellite i, correct the    satellite time-of-transmit Toti for space vehicle (SV) clock    corrections.-   2. For each satellite i calculate the satellite position vector Xi    (Xi,Yi,Zi) and satellite velocity vector XiDot (XiDot,YiDot,ZiDot)    at the corrected Toti. The relativistic corrections for each    satellite can also be calculated at this stage.-   3. Apply relativistic corrections to the corrected Tot's and    recalculate SV position and velocity vectors.-   4. Calculate a starting point for the user position Xu (Xu,Yu,Zu) by    taking the mean satellite position, converting it to a unit vector    and scaling it up by the radius of the earth.-   5. Iterate through the following steps 6 to 19 until the residual    position error is sufficiently small (say less than 100 m) and the    residual time-of-receipt estimate error is also sufficiently small    (say less than 10 ms).-   6. For each raw input observation from satellite i, correct the    satellite time-of-transmit Toti for space vehicle (SV) clock    corrections. Also apply relativistic corrections. (i.e. Steps (1)    and (3)).-   7. Using the corrected Toti's for each satellite, recalculate the    satellite position Xi and velocity XiDot vectors.-   8. Using the most recent user position Xu recalculate the satellite    range Ri and range-rate RRi vectors. Corrections for earth rotation    are also included within the range and range-rate vectors.-   9. An optional step at this point is to is to apply additional    corrections to the measured observations that take into account    propagation delay effects caused by the ionosphere and troposphere.-   10. Using the corrected satellite observations produce a vector yd    of time-of-transmit (or pseudorange) differences scaled to units of    meters. The differences are taken between the first Tot and each    subsequent Tot.-   11. Using the calculated satellite range vectors calculate a vector    of range difference predictions (in units of meters) ydh for each    satellite. The differences are taken between the first range    prediction and each subsequent range prediction.-   12. Calculate a measurement (attitude) matrix M, where each row in    the matrix is the difference between the first LOS vector and each    subsequent LOS vector. Calculate the pseudo-inverse Mpi of the    measurement matrix M.-   13. Calculate the correction to the user position Xu as    dx=Mpi*(yd−ydh).-   14. If the number of algorithm iterations exceeds 3 iterations then    include the following additional steps 15 to 19.-   15. Recalculate the satellite range Ri and range-rate RRi vectors    using the new estimate for the user position Xu (calculated in step    13).-   16. Using the corrected satellite observations produce a vector y of    satellite pseudoranges scaled to units of meters. This is similar to    step (10) except that the vector y is a vector of full pseudoranges    rather than pseudorange differences. Note that this calculation    requires an estimate of the time-of-receipt which could contain an    error that can be solved for.-   17. Using the calculated satellite range vectors calculate a vector    of range predictions (in units of meters) yh for each satellite.    This is similar to step (11) except that the vector yh is a vector    of full pseudorange predictions.-   18. Calculate a 2^(nd) measurement matrix M1 consisting of 2 rows    and Nobs columns, where Nobs is the number of satellite    observations. The 1^(st) column contains the value c, where c is the    speed of light in meters per second. The second column contains    estimate of the satellite range-rate in units of meters per second,    where the satellite range-rate is calculated as the projection of    the satellite range-rate vector onto the line-of-sight vector.-   19. Calculate a dt vector as dt=pseudoinverse (M1)*(y−yh). The    second element of dt is then an estimate of the time-of-receipt    error given the value of the time-of-receipt used to calculate the    pseudorange and pseudorange estimates. This time can then be used to    correct the time-of-receipt of the raw observation and the procedure    repeated.    Further Aiding Source/Receiver Interaction

With the approximate location and previously stored almanac data, theSPS Receiver 1 performs its correlation search to acquire satellitesignals. The almanac data and the approximate location help to constrainthe initial search once at least one satellite has been acquired. Uponacquisition, the SPS Receiver 1 sends a Second Aiding Request 7 to theAiding Source 2. The Second Aiding Request 7 includes information foridentifying the specific set of satellites used by the SPS Receiver 1 indetermining pseudorange differences. In response, the Aiding Source 2determines the precise positions and velocities of the identified set ofsatellites from ephemeris data for the satellites. The determinedpositions and velocities are then sent to the SPS Receiver 1 in a SecondAiding Response 8. Since this elapsed time is known and assuming thatthe latency between transmission and reception of the request for aidingcan be determined it is possible for the aiding source to determine thetime of reception of the satellite signals to within a few tens ofmilliseconds.

Therefore, under this scheme since the Aiding Source 2 provides precisesatellite positions and velocities, the Aiding Source 2 rather than theSPS Receiver 1 needs to be able to determine specific timesynchronization data from the satellite signals and needs to maintain oracquire ephemeris data. Moreover, in order to ensure that the satellitepositions are accurate when received by the SPS Receiver 1 from AidingSource 2, the latency period for the communication between the two mustbe within a few tens of milliseconds. This will ensure a limitation onthe error in the computed satellite locations to a few meters. To thisend, in the preferred embodiment, the Second Aiding Request 7 occurs ata known elapsed time from the instant when the code phases of thesatellite signals latch. Since this elapsed time is known and assumingthat the latency between transmission and reception of the request foraiding can be determined it is possible for the Aiding Source 2 todetermine the time of reception of the satellite signals to within a fewtens of milliseconds.

After receiving the satellite positions and velocities and usingpseudorange and range rate differences, the SPS Receiver 1 then computesa Position and Velocity (PV) solution to determine its precise location,speed, heading, etc. After such determination, the SPS Receiver 1 thensends a Receiver Report 9 to the Aiding Source 2 which includes the rawlocation, speed, heading, height, satellite identifications and thesolution mode used by it (i.e. 3D or 2D with altitude aiding).

Upon receipt of this later information, the Aiding Source 2 may takefurther action. For example, the Aiding Source 2 may use the knownsatellite set and times of transmission, select differential pseudorangecorrections (obtained by any available means) and then compute acorresponding location correction consistent with the reported mode ofsolution. With this later computation, the Aiding Source 2 may thenapply the correction to the location reported by the SPS Receiver toobtain a more current location. This precise location then may be sentto the Call Taker 3 in a Second Aiding Report 10.

As an alternative embodiment of this aforementioned exchange, the SPSReceiver 1 reports the code phase differences to the Aiding Source 2. Inthis event, the Aiding Source 2 could compute a PV solution for the SPSReceiver 1 using a method like that of the SPS Receiver 1.

As described, the scheme requires the SPS Receiver 1 to computepseudoranges without the benefit of having actual time synchronizationdata from the satellite signals. The use of this data is avoided becausethe code phases of the satellite signals are taken as ambiguousmeasurements of the differences in the times of transmissions of thesatellite signals. This is accomplished by measuring the code phases atthe same instant so that there is a common time of receipt. Moreover,the ambiguity resolution needed to convert code phase differences intopseudorange differences is achieved by utilizing the approximatelocation for the receiver obtained from the Aiding Source 2. When theapproximate location is then combined with approximate time from a realtime clock (accurate to say 1 minute) and current almanac data (lessthan about 2 months old in the case of GPS which provides range errorsof less than about 30 km), this permits approximate ranges of thesatellites to be determined to better than half of a code epochmultiplied by the speed of light which is the ambiguity interval for thecode phase differences. By combining the set of approximate ranges andthe set of code phases, unambiguous and precise pseudorange differencesare derived without the need to synchronize the receiver to better thanabout one minute.

Furthermore, consistent with the aiding data minimization objective, theSPS Receiver 1 is able to compute PV solutions without the need forbeing supplied with Doppler information. In traditional devices, suchinformation is used to assist in restricting a carrier frequency searchduring signal acquisition or to predict changes in satellite range as analternative to using the satellite ephemeris data. In one embodiment,the SPS Receiver 1 estimates Doppler information using stored currentalmanac data.

Using this current almanac data, the SPS Receiver 1 estimates theDoppler frequencies of the satellites to an accuracy of better thanabout 250 Hz for GPS given approximate locations of the satellites knownto better than about one hundred kilometers. This is sufficient accuracyto achieve a rapid acquisition provided that the frequency offset of thereference oscillator of the SPS Receiver 1 is known to within a few Hz.To achieve this latter requirement, the reference frequency offset isestimated each time a PV solution is computed and thus it can betracked. Moreover, to the extent that the reference frequency varieswith temperature as well as aging and to the extent that a large changein temperature between PV solutions will lead to a degradation inacquisition performance, the SPS Receiver 1, as described below,utilizes a method that copes with the change and ensures gracefuldegradation.

To ensure the presence of current almanac data without the use of anAiding Source 1, the SPS Receiver 1 must be activated often enough andfor long enough in the presence of strong signals to keep the datacurrent. In the case of the GPS system, the almanac data must be lessthan 2 months old to remain current. To meet this goal under GPS, theSPS Receiver 1 must gather approximately 27 sets of orbital coefficientsover a period of around 2 months. On average it would take around 20 splus signal acquisition time to gather one set and it would take around60 such gatherings to acquire all of the sets. Hence, if the SPSReceiver 1 was activated in the presence of strong signals approximatelyonce per day on average for about 30 s then the stored almanac wouldremain current.

Calculation of Almanac Aiding from Table of Orbit Coefficients

It is common practice to supply aiding information to a GPS receiver inthe form of a full Ephemeris and its time of issue. One possibility isto have the receiver acquire its own Almanac data but Augmentationcoefficients are supplied as aiding information. These coefficientspermit the location and velocity computed from the Almanac data to becorrected to similar accuracy as provided by the Ephemeris coefficients.One disadvantage of this approach is that the receiver has to indicatethe Issue Of Almanac to the aiding source so that the correct aidingdata can be supplied.

An alternative to this approach is to hardcode orbital coefficients fora number of the most common satellite orbits into a lookup table in theGPS receiver. The aiding source then supplies aiding to the receiver fora given number of visible satellites in the following form:

-   -   Satellite ID    -   Reference time of week (a required parameter of any orbit model)    -   Look up number to a table of hard-coded orbital coefficients in        the receiver    -   Corrections to the orbital coefficients and/or additional        orbital coefficients and/or corrections to the output after        using the orbital coefficients.

Because of the compact nature of this data compared to full Ephemerisdata or full Almanac data, it is feasible to structure the aiding in theform of broadcasts transmitted at regular intervals. It would beunnecessary for the receivers to request aiding since they would notneed to supply the Issue Of Almanac and this is another reason whybroadcast coefficients would be feasible. One advantage of usingbroadcasts is that this would significantly reduce system data trafficin the direction toward the network base center, call center or wirelessweb site. Another advantage of the scheme is that the receivers do notneed to acquire Almanac data which means that they need not be operatedin the open at regular intervals to do so.

Calibration of a Local Oscillator

In many Assisted GPS applications rapid acquisition is a keyrequirement. Reference oscillator drift is a major contributor toacquisition time as it causes the “Doppler” frequency search to beincreased to allow for reference oscillator drift. By utilizing theprecise signal framing of a digital communications link, it is possibleto calibrate a local oscillator. This can be performed as follows:

-   f_(LO)=Frequency of Local Oscillator in Hz to be corrected.-   f_(SF)=Frequency of Signal Framing of digital communication link in    Hz.-   Tol_(LOC)=Tolerance of Local Oscillator before Calibration in PPM.-   Tol_(SF)=Tolerance of Signal Framing of digital communication link    in PPM.-   Offset_(LO)=Offset (in Hz) required to calibrate the Local    Oscillator to tolerance Tol_(LOC).-   P=Period of time (in seconds) over which Local Oscillator clock    cycles must be counted in order to calculate Offset_(LO) to    Tol_(LOC).-   N_(SF)=Number of Signal Frames over which Local Oscillator clock    cycles must be counted in order to calculate Offset_(LO) to    Tol_(LOC).-   N_(LO)=Number of Local Oscillator clock cycles counted during P (or    N_(SF) Signal Frames).-   n=1/n is the fraction of a Local Oscillator cycle able to be    measured at the signal framing transitions.

The scheme involves counting local oscillator cycles and fractionsthereof over a period precisely determined by a number of signal framingintervals. The measurement resolution relative to the local oscillatorperiod is a 1/n fraction of the local oscillator period and is easilydetermined with the presence of any hardware clock whose frequency isapproximately n times the local oscillator frequency f_(LO). As anexample, the local oscillator may be f_(LO)=10 MHz and so the presenceof a 50 MHz clock with an accuracy of 1/2n (just 10% in this case) orbetter will permit measurement of the local oscillator phase to thenearest 1/n of a cycle at the signal framing transition. The measurementis repeated at a later signal framing transition and the total number oflocal oscillator cycles and n^(ths) of a cycle is determined. AssumingTol_(SF)<<Tol_(LOC), thenP=10⁶/(n×f _(LO) ×Tol _(LOC))The minimum number of signal framing intervals to be used is given byN _(SF)=Roundup(f _(SF) ×P)where the “Roundup” function provides the next higher whole integer when(f_(SF)×P) is a non integer so that the correction meets or exceeds theaccuracy required.

-   The actual measurement period is N_(SF)/f_(SF)    where N_(LO) cycles (accurate to 1/n of a cycle) are counted.-   The more accurate period for a single local oscillator cycle is    N_(SF)/(f_(SF)×N_(LO))-   The more accurate frequency is thus (f_(SF)×N_(LO))/N_(SF)-   The frequency offset of the oscillator is then given by    Offset_(LO) =f _(LO)−(f _(SF) ×N _(LO))/N _(SF)

This calibration offset can be used as a correction by the GPS receiverfirmware when performing acquisition searches or it can be used tocorrect the oscillator frequency so as to minimize the offset.

Weak Signal Acquisition/Tracking in Presence of Jamming Signals

One of the problems associated with the use of weak SPS signals is thatany SPS system has a limited dynamic range. In the case of the C/A Codesignal of the GPS system, for example, any signal that is weaker by morethan about 20 dB than another signal that is also present may be jammedby the stronger signal. There are 2 main effects of this jamming. First,while attempting to acquire the weaker signal using a sufficiently lowthreshold, the receiver's search sequence will be interrupted byfrequent false alarms because the cross-correlations between the strongsignal and the code generated in the receiver will be often be above thethreshold. Second, although the receiver may be able to acquire andtrack the weaker signal, it will be susceptible to large measurementerrors caused by cross-correlation side lobes from the stronger signaldragging the tracking algorithm away from the true correlation mainlobe.

It is highly desirable to avoid the first of these problems and it isessential to avoid the latter problem as it can result in grosspositioning errors of up to several kilometers. Thus, the SPS Receiver 1should attempt to acquire strong signals before attempting to acquireweaker signals. In this regard, FIG. 2 outlines an example procedure fora GPS receiver that ensures that any strong signals are acquired firstusing a high threshold. A threshold for acquisition of any remainingneeded signals will then be set, in the case of GPS, 20 dB below thestrongest signal acquired. While the FIG. 2 contains informationpertinent to GPS system, its general application would be equallyapplicable to any other SPS system.

Referring to FIG. 2, the device starts with the first request/responseexchange in attempt to acquire the SPS Receiver's 1 approximate locationfrom the Aiding Source 2. If the exchange is successful and theapproximate location aiding data is received, the device sets itsinitial search parameters in step 11 with the goal of acquiring thestrong signals. The parameters for the search are initially set to ahigh threshold and with a short integration period adequate foracquisition at the selected threshold. There is no prior knowledge ofthe code phases of the satellites and therefore the search isunrestricted. The reference frequency offset is assumed to be a priorvalue that was measured when the receiver was previously active (i.e.“old”). In subsequent step 12, with these parameters, the SPS Receiver 1uses a multi-channel device to perform a concurrent search for thestrong signals of all visible satellites.

In the GPS example, conducting such an unrestricted code search with 1channel per satellite at 1 chip per integration period using 2 arms perchannel with 0.5 chip spacing would take 1023/4=256 integration periodsor approximately 1 second. In most cases this would be adequate toacquire any strong signals present.

However, in some instances, the assumed frequency offset of thereceiver's reference oscillator may have changed by a larger amount thancan be accommodated for by the sampling rate of a correlator's outputsamples. Thus, if no satellite signals are acquired during an attemptusing the assumed reference frequency offset, then the offset would beadjusted in step 60 and the search continued without lowering thethreshold. In this way, the offset would be changed systematically toeffect a search over the possible frequency range. The systematic searchwould terminate on the acquisition of one or more satellites at thehighest possible threshold.

If no satellite signals were acquired over the possible frequency rangethen, in step 13, a lower threshold would be set (e.g. 6 dB lower) and alonger integration period would be used (e.g. 4 times as long). Thereference frequency offset would be set back to the previously assumedvalue and the frequency search would be restarted using these parametervalues.

In the original search to locate at least one satellite, if any strongsignals are acquired and additional signals are still needed, then themeasured carrier to noise ratio of the strongest signal acquired wouldbe used to determine both the integration period to be used for asubsequent search in step 14 and the acquisition threshold to be appliedduring the search. For example, if a signal of greater than 50 dBHz wasacquired then the integration period for the subsequent search need notbe any more than 32 ms. This is because it is possible to detect signalsof 30 dBHz or more with an integration period of 32 ms and the thresholdwould have to be set at 30 dBHz or higher to avoid the dynamic rangeproblems previously described.

The reference frequency offset would also be estimated using theapproximate location of the SPS Receiver 1 together with the almanacdata and the measured carrier frequency of the acquired signal. Thisshould obviate the need for further frequency searches.

Since at least one satellite has been acquired, a restricted searchregime can be conducted for the remaining satellites using one channelper satellite since the approximate code phase differences between allof the remaining visible satellite signals and the first one can beestimated. If the accuracy of the approximate position estimate isassumed to be ±10 km then the code phase differences can be estimated towithin ±25, approximately. In the GPS case this would permit all of theremaining satellites to be acquired within 50 * 0.128 s or 6.4 s.However, the search could be terminated once sufficient satellites hadbeen acquired to permit the location to be determined.

In the GPS example, assuming that the second search did not fail, themaximum time taken to acquire enough satellites would thus be 1 s forthe first search for strong signals plus 4 s for the second search forthe first satellite plus 6.4 s for the subsequent search for theremaining satellites. This adds up to 11.4 s. However, typically,acquisition would take less than this (e.g. 1 s+4 s+3.2 s or 8.2 s).

Cancellation of Cross Correlations

A preferred version of the basic idea of canceling strong signals dealswith the problem that the signals are represented with very lowprecision at the input to a correlator and, hence, any scaling of thesignal can only be extremely crudely performed. A preferred variationwhich allows the scaling to be performed at a point where the signal isrepresented by a higher number of bit samples, e.g. 10 bit samples andscaling is more feasible is the following:

The concept involves hardware provision in the correlator forcancellation. FIG. 7 is a schematic diagram showing how a canceler maybe integrated into a correlator. Each channel or certain channels may bereconfigured to provide a canceling service for others. Alternatively,these canceling elements can be dedicated for the purpose. For eachfinger of each channel for which canceling is to be performed, acanceling finger is required.

The canceling finger takes one input from the code generator input tothe prompt finger of a channel tracking a jamming signal. It takes thesecond input from the code generator input of the finger for whichcancellation is to be performed. By correlating these two signalstogether, the canceling finger generates an estimate of thecross-correlation between the two C/A-Codes involved.

This is then scaled in proportion to the magnitude of the jamming signalwhich is obtained from the correlation achieved in the channel trackingthe jammer. The result is an estimate of the value of thecross-correlation experienced in the channel for which cancellation isrequired. Cancellation is then achieved by simply subtracting theestimated cross-correlation value from the correlation measured.

This embodiment has the advantage that the scaling and cancellation areperformed at a point where the signal is represented by approximately 10bits. For this to be so the bandwidth of the signal must have beenreduced by a factor of around 2⁸ or 256 times through a process ofdecimation (integration and down-sampling). It is feasible if allchannels have been downconverted identically and therefore thedownconversion and code mixing steps can be as shown in FIG. 7. The lastchannel specific downconversion step is small but large enough toaccount for the Doppler variation of plus or minus 4 KHz.

Simultaneous Code Phase Determination of Multiple Satellite Signals

To achieve the aiding scheme as described, an SPS Receiver 1 of thepresent invention requires the ability to determine the code phases formultiple satellite signals at the same instant. In this regard, FIG. 3depicts one such SPS Receiver 1. Generally, the SPS Receiver 1 can bebroken down into roughly three parts. The device has a front-end circuit17, three or more correlators 18 and a microprocessor 20 with memory.The following describes the functions of each.

Generally, the front-end circuit 17 serves as the initial signalprocessor as follows. The front-end circuit 17 amplifies, filters, downconverts and digitizes the signal from an antenna so that it is suitablefor processing in a digital correlator 18 and such that the signal tonoise and signal to interference ratios are minimized subject toeconomic and practical realization requirements. The front-end output 19of the front-end circuit could be a complex signal centered at tens ofKHz (in the case of GPS) or a real signal centered at around 1.3 MHz orhigher. The sampling rate would typically be several MHz and thedigitization would be at least 2 bits per sample. In the preferredembodiment an AGC circuit keeps the level of the digitized signalconstant. Since the true signals are spread over 2 MHz in the case ofGPS and are weak signals in any case, this signal is dominated by noiseand the AGC maintains a constant noise level at the output of thefront-end.

Hardware correlators 18 each representing a processing channel for aparticular satellite signal are used separately to further process thefront end output 19 under the control of the microprocessor 20. Withineach correlator 18 a further down conversion 21 (quadrature in thiscase) to nearly DC is performed based on the estimated Doppler offset ofa particular satellite signal and the estimated offset of the crystaloscillator reference frequency driving the correlator. Then, theresulting complex down converted signal 22 is mixed with (i.e.multiplied by) a real binary pseudorandom code signal 23 chosen to matchthat of a particular satellite signal and generated by a code generator24. The code generator 24, controlled by the microprocessor 20,generates the pseudorandom code signal 23 at a selected rate set tomatch the estimated signal Doppler offset given the estimated crystaloscillator offset.

The code generator 24 also generates a late pseudorandom code signal 25that is the same as pseudorandom code signal 23 but at a fixed lag withrespect to the former. This late pseudorandom code signal 25 is alsomixed with the down converted signal 22. The resulting mixed signals 26are then separately processed by decimators 28. Decimators 28 low passanti-alias filter and down sample the mixed signals 26 to a reducedsampling rate. In the case of GPS, the reduced sampling rate isapproximately 1 KHz. This sampling rate may be derived from the localcode rate such that a single sample will be obtained for each codeepoch. However, this is not essential.

When searching in code, the processor 20 either causes the codegenerator 24 to step instantaneously by the required amount at the startof each integration period or changes the code frequency by a knownamount for a precise period of time so as to effect a rapid step in thecode lag. This is the preferred embodiment although in an alternativescheme the code frequency may be deliberately offset while searching sothat the code slews continuously relative to that of the incomingsignal.

When tracking a satellite signal, in this embodiment, the processor 20constantly adjusts the code lag as just described so as to keep thepseudorandom code signal 23 and late pseudorandom code signal 25 fromthe code generator 24 running one ahead (early) and one behind (late)the code of the incoming signal. In other embodiments, the codegenerator 24 may generate a third signal (prompt) that is kept runningsynchronously with the code of the incoming signal or, indeed, there maybe several more signals spanning a lag interval of up to 1 chip(smallest code elements) early and late of the incoming code.

The correlator output samples 29 are read into the processor 20 wherethey are further processed by a signal processing algorithm describedlater in this specification to estimate the amplitude, frequency andphase of the carrier signal. Then, if data is to be extracted becausethe signal is strong enough for that, the phase and frequency areutilized by a separate algorithm that operates on the raw samples toextract the data. Methods for the extraction of this data will beobvious to one skilled in the art.

The frequency of the tracked carrier signals are then used to estimatethe Doppler offset of the carriers and the crystal oscillator offset.The former Doppler offset values are subsequently used to estimate thevelocity of the receiver (and the vehicle in which it may be traveling).

The amplitude of the early and late correlator output samples 29represent estimates of the carrier to noise ratio of the satellitesignal since the noise level is maintained constant by the AGC of thefront-end. When performing a satellite signal search, the amplitudes arecompared to a threshold to determine if the signal has been detected. Ifit has, then an acquisition procedure is commenced. The steps of anappropriate acquisition procedure will be obvious to one skilled in theart.

When tracking, the code phase is adjusted as discussed earlier in orderto keep the average amplitudes of the correlator output samples 29 equalto each other. A similar, but more complex algorithm can be applied if 3or more correlator output samples 29 are present. The nature of thesecontrol algorithms will be obvious to one skilled in the art.

At the end of each integration period the code phase 30 for eachcorrelator 18 are simultaneously latched by a latch element 31 withinthe hardware correlator. The resulting signal represents the code phasemeasurement 32. These code phase measurements 32 are then made availableto the processor 20. The processor 20 then applies a smoothing algorithmto the code phase measurements 32 together with the carrier frequencyestimates. This algorithm is used to reduce the random error in the codephase measurements 32 over time by making use of the precision in thecarrier frequency estimates to predict the changes in code phase fromone integration period to the next. The algorithm also filters thecarrier frequency estimates to reduce their random errors. The carriersmoothing algorithm is described later in this document.

After several seconds of smoothing, (five seconds in the preferredembodiment), the carrier smoothed code phase measurements and thefiltered carrier frequency estimates are passed to the location solverwhich estimates the SPS Receiver's 1 location and velocity. Thealgorithm makes use of precise satellite position data and theapproximate location received from the aiding source. This calculationis described in more detail later in this document. The signalprocessing, carrier smoothing and location solving algorithms are allexecuted by the processor 20.

Modified Search Engine

Search engines vary from chip to chip. One well known type (Solution 1)involves 12 channels with 20 fingers per channel and these channels canbe ganged to provide 240 fingers. In this ganged mode each finger has 8DFT bins thereby providing the equivalent of 1920 fingers all searchingfor the one satellite. Another well known type (Solution 2) involves asearch engine with approximately 16,000 fingers which can be used tosearch for up to 8 satellites simultaneously across the full code spacebut with only one frequency bin. Other types incorporate limited searchengine capacity together with correlators.

In all cases the search engine hardware supports coherent integration,usually with variable integration period, followed by non-coherentintegration over a variable period. FIG. 8 illustrates a preferred 2 binsearch engine architecture. The coherent interval determines thebinwidth but it also determines the coherent processing gain. Thesquaring loss incurred at the end of the non-coherent integration stepdepends on the signal-to-noise ratio prior to squaring and therefore itis desirable to maximize the coherent interval so as to minimize thesquaring loss. However, it is necessary to obtain sufficient bandwidthto cope with residual TCXO error and Doppler error due to initialpositioning error. With longer coherent integration periods this mayrequire frequency searching unless multiple frequency bins are employed.

In a wireless location application a minimum of around 200 Hz bandwidthis required to allow for up to 20 km cell radius and 0.1 PPM residualTCXO error after network compensation (if employed).

Since the Solution 2 hardware does not incorporate a correlator it isnecessary for all of the satellite search engines to commencesimultaneously at the same codephase so that the codephase measurementsare meaningful in a relative sense. This rules out sequential frequencysearches and thus limits the coherent interval that may be employed to 5ms so as to provide 200 Hz bandwidth. If only 4 satellites are acquiredsimultaneously then 2 bins can be produced for each and the coherentinterval can be increased to 10 s. However, with the fading typical ofweak signal environments, this would not produce good performance.

Where correlators are employed, tracking firmware may be used to tracksatellite signals following acquisition using the search engine. Thisprovides more freedom in the optimization of the search algorithm.Longer coherent integration periods may be employed because the searchmay be undertaken sequentially and satellites may be acquiredsequentially. Once the first satellite has been acquired the searchrange may be restricted in code space to reduce acquisition time or therequired search engine capacity.

For an architecture like Solution 1 the Search Engine mode is employedfor the first satellite after which it is far quicker to use thecorrelators with acquisition firmware based on the algorithms disclosedin this document to perform in parallel a restricted sequential codesearch for all of the remaining satellites. The search may be repetitiveto allow for fading. Several of the 20 fingers may be used in parallelto speed the search process although the processing load may beprohibitive if more than 6 fingers are used. (The fingers are used inpairs to minimize processing load and to optimize detectionperformance.)

Searching using conventional correlators with the acquisition algorithmsof the invention is a sequential process. However, because theintegration periods required for given sensitivity are far less usingthe processing scheme of the invention than using a search engine, thechoice of correlators or Search Engine for acquisition depends on theavailable search engine capacity. For the first satellite the searchengine is invariably quicker. For subsequent satellites it may bequicker to use correlators.

A special search engine configuration may be employed to integrate withthe processing algorithms of the invention. This allows the searchengine to take advantage of the greatly reduced integration periods ofthe invention at the cost of more frequency bins. The fingers are usedin groups of 30 to provide consecutive DFT bins to allow for the databandwidth and frequency uncertainty with the narrower binwidthcorresponding to a coherent integration period of 128 ms. Prior tomagnitude squaring, an extra processing block is required but the extraprocessing capacity required represents an additional overhead of only10% of the coherent processing capacity requirement. After non-coherentintegration the peak bin and adjacent bins are passed to the firmwarefor additional processing to estimate carrier magnitude and frequencyfor detection and handover to the correlator. FIG. 9 illustrates themodified search engine architecture.

Using 1 ms minimum and 19 ms maximum coherent integration periods, thenon-coherent integration time required for detection of a signal using aconventional search engine is given as in Table 1. (A choice of 19 msprovides a long coherent integration period below 1 bit and ensures thatthe bit periods slide continuously through the coherent integrationperiods. This results in a loss of 1.2 dB but avoids any requirement fordata synchronization.) For 5 ms coherent integration the non-coherentintegration periods required would be much longer.

TABLE 1 SEARCH ENGINE INTEGRATION TIME VS CONDITIONS RF SIGNAL IF C/NoINTEGRATION CONDITION LEVEL (Dbw) (dBHz) PERIOD (ms) Open skies,directly above −150 54 1 Open skies, high elevation −155 49 1 Openskies, mid elevation −160 44 2 Open skies, low elevation/ −165 39 6indoors, very strong signal Open skies very low −170 34 19elevation/indoors, strong signal Indoors, moderately strong −175 29 88signal Indoors, weak signal −180 24 599 Indoors, very weak signal −18519 5270 Indoors, extremely weak −190 14 51500 signal

The lower the signal to noise ratio prior to squaring the higher thesquaring loss and this is the reason that the required integrationperiod rises exponentially. This, in turn, is the reason why it isessential to acquire satellites in parallel and hence why multiplesearch engines are needed.

However, the integration period required for the processing scheme ofthe invention is less because much longer coherent integration periodsare employed thereby reducing the squaring losses.

Table 2 compares the integration periods and acquisition times of 3schemes for very weak to extremely weak signals. The hardware employedin the 3 schemes is a search engine of around 16,000 fingers plus, say,12 correlators. This is just an example. Similar analyses can beperformed for other search engine configurations and the results in allcases favor the use of the algorithms of the invention either running infirmware using the correlators or in the form of a hardwaremechanization as in FIG. 9.

The first scheme uses the Search Engine only with 5 ms coherentintegration periods. The second scheme uses the search enginesequentially with 19 ms integration periods for acquisition and thecorrelators for tracking. Only 2 steps are required as all remainingsatellites can be acquired in parallel after the first satellite isacquired. The full search engine is used to search for 2 satellites inparallel in the first step. The third scheme uses a DFT search engineand a hardware mechanization of the algorithms of the invention forsequential acquisition along with correlators and firmware based on thealgorithms of the invention for tracking. Three search steps arerequired, 2 for the first satellite and one for the remainder.

TABLE 2 INTEGRATION PERIOD & ACQUISITION TIME FOR SEARCH ENGINE Vs THEINVENTION SCHEME 1 Search Search SCHEME 2 Engine Engine AcquisitionSCHEME 3 Acquisition Integration Time With Worst Case Modified Time WithPeriod With 19 ms Integration Search 5 ms 19 ms Coherent IP Period (s)Engine Sensitivity Coherent IP Coherent IP (s) and For The Acquisition(dBW) (s) (s) Tracking Invention Time (s) −185 to −187 38.2 15.2 30.44.2 12.6 −186 to −188 60.3 24.0 48 5.4 16.2 −187 to −189 95.1 37.8 75.68.3 24.9 −188 to −190 154.7 60.2 120.4 13.1 39.3

To allow for 20 km of initial position uncertainty we require a searchrange of at least 60 chips after acquisition of the first satellite.This determines the number of search engine fingers required afteracquiring the first satellite. This is 480 per satellite for scheme 2and 3,600 for scheme 3 which means that, in both cases, four or more ofthe remaining satellites can be acquired in parallel.

As we have seen, Scheme 1 requires the longest acquisition time andhence the highest energy requirement for the first fix. Moreover,subsequent fixes require equally as much energy as the satellites mustbe acquired from scratch again. Using the invention for tracking,however, the energy required for subsequent fixes can be greatly reducedin 2 ways. Firstly, the power hungry search engine can be turned off.Secondly, the entire receiver can be duty cycled over quite long periodsto achieve considerable savings. A Timer must be used to turn thereceiver back on after a period precisely known to within a fewmicroseconds. Then the code generators can be shifted close to thetracking codephase prior to a brief reacquisition search using theinvention implemented in firmware. Since only the correlators are usedthe energy requirement is substantially less than would be the case forthe first fix.

Satellite Signal Processing Algorithms

As previously noted, special processing of the weak satellite signal isrequired in order to determine its code phase differences for purposesof calculating a PV solution from them. To this end, the inventionutilizes an algorithm to measure amplitude, frequency and phase of thesatellite signal. The invention also applies a smoothing procedure tothe code phase measurements and the carrier frequency estimates toreduce random errors occurring over time. Finally, a formula is appliedto convert the code phase differences into a precise position andvelocity solution. Each algorithm is addressed in turn.

(1) Estimating Amplitude, Frequency and Phase of Weak Satellite Signal

Any SPS Receiver 1 employing a hardware correlator 18 with severalsignals in each channel is required to estimate the amplitude of thecarrier in each of those several signals in the presence of the data. Ina conventional receiver a phase locked loop or a frequency locked loopand a delay locked loop control the frequency of the final downconverter 21 and the code generator 24 respectively. In the weak signalcase however, the signals are too weak to permit lock-in without the useof some sort of aiding from an auxiliary algorithm. The signalprocessing algorithms of the present invention could be used asauxiliary algorithms for acquisition or they may be used independentlyof any phase-locked or frequency-locked loop for both acquisition andtracking.

With strong signals it is possible to detect a signal from individualcorrelator output samples 29 with sufficient reliability to permitlock-in. In the weak signal case, the correlator output samples 29 areso noisy that the signal is indistinguishable from the noise unless thecorrelator output sampling rate is extremely low (e.g. 8 Hz for GPS).However, to avoid the Decimators 28 filtering out the residual carrierat such a low sampling rate, the Doppler frequency and the crystaloscillator offset would have to be known to an impractical highprecision. Accordingly, a higher correlator output sampling rate needsto be retained and a suitable algorithm is required to estimate theamplitude of the residual carrier signals in the code phase measurements32.

FIG. 4 depicts a flow chart for an algorithm used to measure amplitude,frequency and phase of the signal from each of the early and late mixedsignals 29 of the correlator 18 according to one embodiment of thisinvention. Referring to FIG. 4, the procedure involves the use of a FastFourier Transform 33 applied to a block of samples from the code phasemeasurements 32 of the correlator 18. The effect of this algorithm is tocompress the residual carrier signal into a few bins of the FFT output34. Thus, whereas the satellite signal is undetectable when its energyis spread across all of the time domain samples contained in the codephase measurements 32, it is detectable in the bins of the FFT output34. However, its amplitude is not readily estimated because the datamodulation splits the satellite signal between several adjacent bins ina semi-random manner depending on where the transition falls in relationto both the integration period and the phase of the residual carrier atthat instant.

Nevertheless, having detected the probable presence of a satellitesignal it is possible to reject much of the noise by applying a windowoperation 35 on the bins of the FFT output 34 centered on the peakvalue. More distant bins may be discarded completely. This is simply afiltering operation and significantly improves the signal-to-noise ratioprior to non-linear processing 36 of the window-filtered signal 37 toeliminate the data transitions. The remaining windowed bins in thewindow-filtered signal 37 may be processed in one of several ways inorder to estimate the amplitude in the presence of the noise and data asfollows:

1. The discarded bins may be zero filled and the complete set of binscan then be inverse transformed back to the time domain. This wouldresult in a set of time domain samples with significantly improvedsignal to noise ratio which may then be processed such as to effectlock-in of a phase locked loop or delay locked loop.

2. An inverse transform may be effected as described above and the timedomain samples may be squared to remove the data. The amplitude of theresulting signal could then be estimated by transforming back into thefrequency domain using a DFT to obtain a few bins centered on theprevious FFT peak and applying any estimation algorithm suitable forestimating the amplitude and frequency of a cissoid embedded in noisebased on the values of several FFT bins. It is important to note thatone effect of squaring the signal will be to double the frequency of theresidual carrier signal such that it may be aliased by the samples. Thisambiguity will need to be resolved when determining the frequency.

3. The vector of remaining windowed bins may be autoconvolved to removethe data in an autoconvolution process 36. This is equivalent tosquaring in the time domain but, if the number of bins in windowfiltered signal 37 is small compared to the size of the FFT 33, then theautoconvolution process 36 may be less computationally costly than theprocess described in option 2 above. The autoconvolved samples 38 canthen be processed by any estimation algorithm 39 suitable for estimatingthe amplitude and frequency of a cissoid embedded in noise based on thevalues of several FFT bins. Again it is important to realize that thefrequency corresponding to each bin has been doubled by theautoconvolution process 36 and the bin width has thus been effectivelyhalved for the purposes of frequency estimation.

An embodiment of the invention employs option 3 for estimation of theamplitude and residual carrier frequency. The RF carrier frequency isestimated as follows:Fc=Fd1+Fd2+(Np1+(Np2−Nnom+Nψ)/2)*Fbin−ψFxo*Fnom/Fxo

Where:

Fc is the RF carrier frequency;

Fd1 is the total frequency shift due to the front-end down conversion;

Fd2 is the frequency shift due to the down conversion in the correlator;

Np1 is the bin number (between −N/2 and (N/2+1)) for an N-point FFT) ofthe peak in the FFT (which is the center bin of those extracted forperforming the autoconvolution);

Np2 is the bin number of the peak bin in the autoconvolved bins computed(which is the center bin of those extracted for estimating the amplitudeand frequency);

Nnom is the bin number of the nominal peak bin in the autoconvolved binscomputed (i.e. corresponding to zero lag);

N ψ is the frequency adjustment (in bins and fractions of bins relativeto Np2) estimated by analysis of several adjacent bins of theautoconvolved bins computed;

Fbin is the bin width of the original FFT;

ψFxo is the crystal oscillator offset from its nominal frequency;

Fnom is the nominal RF carrier frequency of the signal; and

Fxo is the nominal crystal oscillator frequency.

While the signal is being tracked it is possible to reduce thecomputational load of the signal processing, if this is desirable, byutilizing the precise value of the residual carrier frequency asestimated in the previous integration period. For example, only thosebins used in the previous integration period need be computed.

Ensemble Averaging Scheme

The coherent algorithm is limited in sensitivity owing to the fact thatgains resulting from the reduction of binwidth are offset by squaringlosses. A more refined averaging scheme has been tested and seemspreferable.

The basic concept of this scheme is to extract the signal power spectrumfrom the noise by averaging. Because the phase information is lost incomputing the power spectrum, the MacLeod estimation algorithm must bereplaced by the less efficient (in an estimation performance sense) Jainalgorithm for estimating amplitude and frequency for detection andcarrier tracking. However, the consequent degradation in estimationperformance is more than offset by the improved sensitivity possible.Nevertheless, when shorter integration periods are required, the MacLeodscheme should be retained.

To avoid excessive memory usage for storing FFT arrays, the scheme mustoperate in the fashion of a moving average rather than by blockaveraging. This implies running a filter on the squared magnitude of theautoconvolution array for each channel. For smooth startup, such afilter should be of a simplified Kalman style.

The first difficulty to be faced in implementing such a scheme is thatthe autoconvolutions are normally performed on windowed FFT values andthe window is centered on the peak value of the FFT during acquisition.This is not the case when tracking as the center of the window ispredetermined. Hence the scheme outlined above is very suitable fortracking but needs augmenting for acquisition as the peak picking of theindividual FFTs will not be reliable at the low signal levels to betargeted.

The solution proposed for this problem is to filter the squaredmagnitudes of the FFT bins in the same fashion as for theautoconvolutions when acquiring. One way to facilitate this would be todwell for several FFT periods at each code search step. An alternativeapproach would be to step very slowly at each FFT period. (ie for N-foldaveraging apply 1/Nth of the normal search step size.) Theautoconvolution and later processing could be performed at each step asnormal with no ill effects. Since the steps will be very small, thesignal spectra will be present for many steps, consistent with theaveraging period, as required. There will be a lag between the signalappearing and actually being incorporated into the autoconvolutionaverage because the FFT average may not produce the correct peaksinitially. However, this should not pose a serious difficulty and can betaken account of by adjusting the capture process as necessary.

Finally, we need to consider the impact of this new scheme on thetracking control algorithms. The approach proposed is to perform partialcontrol adjustments consistent with the averaging period. For example,if the averaging period is N FFTs then the feedback at each should beapplied with 1/Nth of the normal gain.

Simulations of block averaging were conducted to confirm the increasesin sensitivity expected. An FFT size of 128 was used with windows of 15bins before and after autoconvolution. The Jain algorithm was used toestimate the amplitude. Statistics were accumulated over 1,000 runs.

The mean, standard deviation and 99th percentile value of the amplitudeestimates were computed with no signal present. Then the signalamplitude required to make the tenth percentile value equal to the NoSignal 99^(th) percentile was determined by iteration. The mean andstandard deviation of the amplitude estimate were recorded for thisactual signal amplitude. This whole process was repeated for manydifferent averaging periods.

Table 3 shows the results obtained form the simulations. The predictedsensitivity is consistently nearly 3 dB better than that predicted usinga detailed spreadsheet analysis. This is because the simulation does notsimulate the losses due to quantization and correlation offset whichamount to 1.8 dB before squaring and more after squaring. After takingthis into account the simulations confirm the analysis well.

One problem that is evident is that the signal amplitude estimates driftfurther and further away from the true amplitudes. At the same time thenoise amplitude standard deviation decreases more rapidly than the meannoise amplitude. This suggests that the amplitude estimates could all beadjusted by subtracting a factor that depends on the value of N, theaveraging length. The table shows the effects of applying such anadjustment based on the empirical results. This will not affectdelectability and has no other adverse characteristics. Therefore, it isproposed to use a look up table based on these results for the purposeof applying the adjustment. (Note that the adjustment for N=1 should be0.0.)

TABLE 3 ANALYSIS OF SIMULATION RESULTS Adjusted Adjusted AmplitudeAmplitude Amplitude Amplitude Measurement Measurement MeasurementMeasurement Detectable Detectable Predicted With Expected With With WithSignal C/No Sensitivity Noise Only Noise Noise + Signal Amplitude NoiseOnly Noise + Signal Adjusted Amplitude (dBHz) (dBHz) Mean StDev StDevMean StDev Threshold Offset Mean StDev Mean StDev Threshold 11.2 24.326.7 7.37 0.79 0.79 11.51 1.48 9.40 0.31 7.06 0.79 11.2 1.48 9.09 8.822.3 25.0 6.81 0.59 0.56 9.61 1.01 8.30 0.81 6 0.59 8.8 1.01 7.49 7.220.5 23.3 6.43 0.44 0.40 8.47 0.7 7.50 1.27 5.16 0.44 7.2 0.7 6.23 5.918.8 21.7 6.12 0.32 0.28 7.59 0.48 6.90 1.69 4.43 0.32 5.9 0.48 5.21 5.117.5 21.2 6.05 0.29 0.25 7.12 0.41 6.80 2.02 4.03 0.29 5.1 0.41 4.78 517.3 20.4 5.95 0.25 0.21 7.01 0.35 6.60 2.01 3.94 0.25 5 0.35 4.59 4.917.2 20.1 5.92 0.23 0.20 6.94 0.33 6.50 2.04 3.88 0.23 4.9 0.33 4.46 4.215.8 19.3 5.81 0.18 0.15 6.55 0.23 6.20 2.35 3.46 0.18 4.2 0.23 3.85 3.614.5 17.2 5.7 0.12 0.10 6.24 0.17 6.00 2.64 3.06 0.12 3.6 0.17 3.36 3.113.2 16.1 5.66 0.1 0.08 6.06 0.12 5.90 2.96 2.7 0.1 3.1 0.12 2.94

The idea is to filter the autoconvolution and the FFT values. This meansthat there has to be a stored array for each channel containing thefiltered value. One may as well use the same structure as for AutoConvexcept that the .Bins are real rather than complex. Use a simplifiedKalman style approach to starting the filter but stop the convergencewhen the required Gain factor (<1) is reached.

The required Gain factor would be derived from a configurationparameter. It corresponds to the required sensitivity improvement interms of signal power. If it is set to 1 then no filtering is needed.

Note that, when searching, the step size at each step should be afraction (also 1/G) of the normal chip step rather than just oneintegration period.

It is proposed to use the required sensitivity in dBW as theconfiguration parameter. We could have one for tracking and another foracquisition. The Table 4 shows how G (=1/Gain) relates to the requiredsensitivity.

TABLE 4 FILTER GAIN VERSUS SENSITIVITY Required Required Sensitivity(dBW) Sensitivity (dBHz) G −182 22 1 −185 19 5 −186 18 7 −187 17 14 −18816 19One wouldn't choose −188 for acquisition using a conventional correlatorbecause of how slow the acquisition would be. However, one could chooseit for tracking based on the theory that, if you need such sensitivitythen you must be indoors and moving at walking pace. Alternatively, amodified search engine, as discussed elsewhere in this document, may beused for acquisition using this scheme.

Finally, G should be limited by the 20 dB threshold limitation also.This should override the configuration parameter.

The following pseudocode shows how the filter would be applied for theautoconvolution.

if (Tracking)     Greq = Gtracking   else     Greq = Gacq;   if (Greq< 1)     {     if ( first time )       {       FiltAutoConv(Chan).Bins =AutoConv.Bins       G = 1       }     else       {       K = 1/(1+G)      FiltAutoConv(Chan).Bins = FiltAutoConv(Chan).Bins +K*(Magsquared(AutoConv.Bins) − FiltAutoConv)       if (Greq < G)        G = (1−K)*G       }     FiltAutoConv(Chan).Peak =FindPeak(FiltAutoConv(Chan));     FiltAutoConv(Chan).FFTPeak =FFTPeakA.Peak;    /*------------------------------------------------------------------*/    /* Perform Jain Signal Analysis.    Jain(&FiltAutoConv(Chan),pEstSignal);     }   else     {

The algorithmic enhancement to the signal processing scheme avoids theneed for impractical FFT lengths without unnecessarily limiting thesensitivity. The technique involves averaging of the power spectrum atboth the FFT and autoconvolution stages. To keep memory requirements topractical levels, the scheme can be implemented using a filteringapproach.

A simulation study has confirmed the effectiveness of the scheme interms of improving the sensitivity with performance close to thatpredicted by detailed spreadsheet calculations. The study also yieldeddata that has allowed an amplitude adjustment calculation to be devisedinvolving the use of a lookup table.

An implementation scheme has been proposed and implemented in prototypeform with a significant improvement in sensitivity resulting.

(2) Signal Smoothing

While the signal is tracked the code phase and frequency estimates maybe refined by a carrier smoothing process prior to being passed to thelocation solver. FIG. 5 depicts the flow of such a process. Thealgorithm illustrated is a block computation applied to the storedestimates from several integration periods (arbitrarily labeled from Jto N) to compute a single set of measurements representing refinedmeasurements for the final integration period.

In the preferred embodiment, the algorithm processes differences betweenthe estimates from one satellite and those from all the others ratherthan processing the absolute estimates for individual satellites. Thereason for this is that in the preferred embodiment of the locationsolver, differences rather than absolute estimates are used becausedifferences can be processed without the need to take account of thefrequency offset of the reference oscillator.

All of the frequency difference estimates 40 corresponding to aparticular satellite signal are simply averaged in step 42. The averagecarrier frequency difference 43 is then used to predict forward inprediction step 44. This prediction step 44 is used for all but thelatest code phase differences 41 for that satellite to the latestmeasurement instant before averaging them in step 45. The predictionstep 44 uses an estimated Doppler offset of the code 47 determined fromstep 46 which is based upon the difference between the Doppler offsetsof the carrier frequency (Fc1−Fc2). With the estimated Doppler offset ofthe code 47, step 44 is used to predict forward the code phasedifferences 41 by N integration periods using the following formula:ψp1−ψp2=Frac(ψm1−ψm2+N*Tip/(Te*Fnom/(Fc1−Fc2))

where:

ψp1−ψp2 is the predicted code phase difference 48 expressed as afraction of a code epoch;

the Frac function yields the fractional component of the real argument;

ψm1−ψm2 is the measured code phase (41) to be predicted forward;

Tip is the nominal integration period;

Te is the code epoch period as determined from the tracking algorithm.

All other quantities are as previously defined.

The predicted code phase differences are then averaged in step 45 toproduce an average code phase difference 49.

(3) Position and Velocity Solution Computation Using Code PhaseDifferences

As previously discussed, a PV solution is computed by the SPS Receiver 1after code phase differences have been processed by the aforementionedsmoothing algorithm. FIG. 6 illustrates the PV solution process used bya preferred location solver. The location solver, which computes thelocation and velocity using the refined average carrier frequencydifferences 43, the average code phase differences 49, the precisesatellite positions 52 and the approximate receiver location 54 asfollows.

In step 50, approximate ranges 51 to all of the satellites for whichthere are measurements are computed. Since the satellite positions 52are supplied by the Aiding Source 2, this step simply involves thevector difference of the Cartesian coordinates of the satellitepositions and the approximate location 54, which was also supplied bythe Aiding Source 2. The vector magnitudes of these vector differencesare approximate ranges 51.

In step 55, the epoch ambiguities are resolved and the average codephase differences 49 are converted into pseudorange differences 56. Allof the approximate ranges 51 are subtracted from the approximate rangeof a reference satellite (as selected for computing the code phasedifferences) to give approximate range differences. These are saved forlater use as well as being used in resolving the epoch ambiguitiesaccording to the following formula:P1−P2=int[(R1−R2)/c*Te−(Q1−Q2)+0.5]+(Q1−Q2)

Where:

C is the speed of light;

Te is the nominal epoch period;

P1 and P2 are the pseudoranges 56 of satellites 1 and 2;

R1 and R2 are the approximate range estimates for the same twosatellites;

Q 1−Q 2 is the code phase difference between the same two satellites.

In step 58, the range rate differences 57 are calculated from theDoppler affected average carrier frequency differences 43. The rangerate differences 57 are computed according to the formula:d(R1−R2)/dt=−c*(Fc1−Fc2)/Fnom

where:

c is the speed of light and

d(R1−R2)/dt is range rate difference 57 between satellite 1 andsatellite 2.

In one alternative embodiment of the invention, the current locationestimate 60 and current velocity estimate 61 are computed in step 59 bya method similar to that for a single update of a Kalman navigationfilter in a traditional SPS receiver. In fact, if time allows, a truenavigation filter can be run for several updates to further refine theestimated PV solution. (However, in this case the satellite positionswill need to be updated from the Aiding Source 2.) The Kalman gainmatrix, K, is given by the well-known equation:K=PM ^(T)(MPM ^(T) +R)⁻¹andX=X _(INIT) +K(Y−Y _(PRED))

where Y is the measurement vector and X is the solution state vectorcontaining the location and velocity estimates.

Using this equation, the initial state vector, X_(INIT), is set to theapproximate location from the aiding source with zero velocity. Thefirst prediction vector, Y_(PRED), is set to the approximate rangedifferences derived from the approximate ranges 51 and zero for therange rate differences 57.

The state covariance matrix, P, is initialized to a diagonal matrix withthe entries representing the estimated variances of the approximatelocation and velocity estimates. The location variance estimates may beobtained from the Aiding Source 2 or a fixed value could be used. Theinitial velocity estimate is zero and its variance depends on theapplication. The measurement variances can be estimated as in aconventional receiver except that the measurement variance matrix, R, isno longer diagonal. The fact that differences with a reference satellitewere computed means that the covariance terms between the satellites arehalf of the value of the variance estimates rather than zero as in aconventional receiver. The approximate location (54) and the satellitepositions (52) can be used to determine the direction cosines and thedirection cosine differences form the rows of the measurement matrix, M.

We claim:
 1. In an SPS system for identifying the location of a receiverin the presence of satellite signal attenuation comprising having aplurality of orbital satellites sending synchronized encoded signals ona carrier frequency wherein said encoded signals have repeated epochscontaining synchronization data: a receiver for detecting, acquiring,and tracking a set of the encoded signals and simultaneously determiningthe code phases of said set with respect to said epochs; and an aidingsource to transmit information to the SPS system, wherein said receiveris comprised of a correlator with an integrated canceller, wherein saidcanceller is used to reduce the amplitude of strong signals, wherein acanceling finger is provided for each channel for which canceling is tobe performed and said canceling finger takes one input from the codegenerator input to a prompt finger of a channel tracking a jammingsignal and a second input from a code generator input of the finger forwhich cancellation is to be performed.
 2. The SPS system for identifyingthe location of a receiver of claim 1 wherein the cancellation isperformed at a point where the signal is represented by approximately 10bits.